Vehicle cameras are used in particular to detect vehicle surroundings in front of the vehicle, to represent an image on a display unit, to analyze the images detected and for use in driver assistance systems, objects being identified in the image detected. Thus, for example, additional traffic participants, lane markings, traffic signs and traffic lighting may be ascertained. To evaluate the relevance of objects, their color is also important in particular. Thus with simultaneous occurrence of yellow and white lane markings, for example, in a construction area, the yellow markings generally have priority. In addition, light signals may be assigned according to their color value, rear lights having a red color value being differentiated from headlights having a white or yellow color value, for example, and taillights having a yellow color value. Traffic signs and traffic lighting, such as traffic lights and flashing lights, are also relevant in accordance with their particular color value.
It is believed to be understood in this regard that a color classification of the objects detected may be performed. However, the color in the image detected may be represented differently because of the color temperature of the surroundings, which depends on the color of the lighting and the average color value. Different color temperatures of the image detected may result first of all due to different lighting, e.g., depending on the time of day, different road lighting and also due to tints of the vehicle windows behind which the vehicle cameras are generally mounted. The tints of the vehicle windows are initially unknown because they may vary greatly, depending on the model, and to some extent may vary as a function of the position on the vehicle window. In general, the assumption of a “gray world” in which the histograms (statistical data) of the color pixels and intensity pixels are uniformly occupied over the entire image is not correct. In this regard, a color temperature determination of the detected image area is performed in more complex systems to perform a white balance in which the color temperature of the surroundings is subtracted out, i.e., corrected, to be able to correctly classify the color of the object.
For differentiation of colors, color masks whose filter pixels have a specific color value, i.e., a specific transmission behavior in the optical wavelength range, are usually placed in front of the sensitive sensor pixel area. In general, the color masks have specific color patterns, which are formed by periodic repetition of specific basic patterns, for example, as blocks of four pixels each having specific color values, e.g., R (red), G (green), B (blue). Patent documents JP 2004304706 A and WO 2009/027134 A1 refer to such color masks having specific color patterns. However, due to the use of such color patterns, the local resolution decreases because a block of four pixels having different color values is used for one pixel. In addition, due to the filtering, the intensity of the incident light declines and thus its sensitivity also declines. In nighttime applications, for example, a light control function for automatically switching between high beam and low beam, remote light sources must be detected in some cases, which requires a high resolution and high sensitivity. However, the local resolution is reduced in particular in full-color patterns, e.g., RGGB, and the intensity of the incident light is diminished. Partial color patterns, as described in WO 2009/027134 A1, form a compromise between color classification by the particular color filter pixels and resolution or high sensitivity due to the transparent filter pixels.
Thus, in addition to the accuracy of the color determination, the local resolution and light sensitivity as well as the local resolution of the color values or chrominance and the local resolution of the brightness are still relevant features for vehicle cameras and driver assistance systems.